Improving Methods for Single-label Text Categorization

As the volume of information in digital form increases, the use of Text Categorization techniques aimed at finding relevant information becomes more necessary. To improve the quality of the classification, I propose the combination of different classification methods. The results show that k-NN-LSI, the combination of k-NNwith LSI, presents an average Accuracy on the five datasets that is higher than the average Accuracy of each original method. The results also show that SVM-LSI, the combination of SVM with LSI, outperforms both original methods in some datasets. Having in mind that SVM is usually the best performing method, it is particularly interesting that SVM-LSI performs even better in some situations. To reduce the number of labeled documents needed to train the classifier, I propose the use of a semi-supervised centroid-based method that uses information from small volumes of labeled data together with information from larger volumes of unlabeled data for text categorization. Using one synthetic dataset and three real-world datasets, I provide empirical evidence that, if the initial classifier for the data is sufficiently precise, using unlabeled data improves performance. On the other hand, using unlabeled data actually degrades the results if the initial classifier is not good enough. The dissertation includes a comprehensive comparison between the classification methods that are most frequently used in the Text Categorization area and the combinations of methods proposed. Palavras-chave Classificação de Texto Conjuntos de Dados Medidas de Avaliação Combinação de Métodos de Classificação Classificação Semi-supervisionada Classificação Incremental